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Project on Unmanned Aircraft in the NAS Final Review Panel Meeting Integration of Unmanned Aircraft into the National Airspace System A Project Course by Carnegie Mellon University Dept. of Engineering and Public Policy Dept. of Social and Decision Sciences May 1, 2007 Expert Review Panel

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Project on Unmanned Aircraft in the NAS

Final Review Panel Meeting

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Integration of Unmanned Aircraft into the National Airspace System

A Project Course by

Carnegie Mellon University

Dept. of Engineering and Public Policy

Dept. of Social and Decision Sciences

May 1, 2007

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Expert Review Panel System

  • Tom Curtin, AUVSI

  • Bret Davis, AUVSI

  • Lexa Garrett, America West Airlines

  • Jim Geibel, GAO

  • David Gerlach, FAA

  • Tom Henricks, Aviation Week

  • Ramon Lopez, Aurora Flight Sciences

  • Edmond Menoche, GAO

  • Rene Rey, FAA

  • Melissa Rudinger, Aircraft Owners & Pilots Assn.

  • James Sizemore, FAA

  • Larry Thomas, GAO

  • Dyke Weatherington, DoD/OSD

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Purpose of CMU Project Courses in Technology and Policy System

  • Analyze a “real world” policy problem involving technology

  • Combine diverse information and analytic frameworks to derive policy insights

  • Learning objectives:

    • Problem decomposition, structuring and formulation

    • Interdisciplinary problem solving

    • Communication

    • Teamwork

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Contributors to our UAS project System

  • 20 undergraduates majoring in:

    • Engineering

    • Social Science

    • Business Administration

  • 3 Ph.D. student managers

  • 3 faculty advisors

  • Expert review panel

  • Other experts

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Background for this Project System

  • Increasing demand for UA

    • Military (many current uses)

    • Civilian (many potential uses)

  • Federal Aviation Administration (FAA) is developing a roadmap for integrating UA into the NAS

  • A few of the issues to be addressed:

    • Safety and reliability

    • Public acceptability

    • Market viability

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Analysis Areas System

  • Economics

    • How cost-effective are UA compared to alternative means of providing specific services?

  • Risk, Technology and Standards

    • What are the regulatory implications of different approaches to “equivalent level of safety?”

  • Public Awareness and Perceptions

    • Are risks of UA of greater public concern than risks of manned aircraft?

  • Governance

    • How can the current system for deliberation and decision-making on UA access be improved?

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Project Outcomes System

  • ~16 person-months of research completed across the four focus areas

  • Economic model of market viability

  • Risk model of fatality implications of UA introduction

  • Better understanding of public awareness & risk perception

  • Actor & “roadblock” analysis yields insight on deliberative process for UA integration

  • Regulatory & policy recommendations

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Economics System

Team Members:

Nathan Diorio-Toth

Feng Deng

Reiko Baugham

Victoria Morton

Brad Brown

Team Manager:

Ryan Kurlinski


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Purpose System

Assess the market viability of UAS applications using relative cost effectiveness

Assess the effect of various regulatory measures on the market viability of UAS applications


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Goals System

  • Develop UAS cost model

    • Cost components

      • Airframe

      • Communications

      • Insurance

      • Pilot

      • Etc.

  • Apply cost model to chosen applications and alternatives to compare cost

  • Examine sensitivity of overall cost to changes in each cost component

  • Estimate cost implications of different regulatory measures and technology improvements


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UAS Applications System

  • Weather Reconnaissance

    • Alternative:

      • WC-130J Hercules: high-wing, medium range aircraft

  • Pipeline monitoring

    • Alternative:

      • Concentric sensors: pressure sensitive sensors

  • Localized Surveillance

    • Alternative:

      • Traffic Helicopter: e.g. Bell JetRanger


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Analysis Method System

  • Used triangular distributions to assign probable ranges to each input cost

    • From this, generated a Probability Density Function

    • Probability Density Function shows the entire range of possible costs with the associated likelihood of each cost

    • Allows analysis of the most probable cost advantages


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Importance Analysis System

Contribution of uncertainty in each input to uncertainty in total cost

Triangular probability distributions of all input variables

Economic Model

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Weather Reconnaissance System

Analyzed the use of Aersonde UAS for Weather Reconnaissance vs. the use of the WC-130J Hercules

Aerosonde UAS currently in use for Weather Reconnaissance

Hercules WC-130J currently in use by Keesler Air Force Base


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Results: SystemWeather Reconnaissance




Probability Density













Alt Cost-UAS Cost

Probability Density of UAS Cost Advantage

($/flight hour)


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Results: SystemWeather Reconnaissance




Importance in Alt Cost-UAS Cost




Safety Technology Cost

Component costs


Cost per gallon

Hours per year

Com-Link Cost

Insurance Rate

Gallons per Hour

Operational lifetime

Alt Cost-UAS Cost Inputs

Importance Analysis of Model Inputs

Mission Hours per Year

Operational Lifetime

Com-Link Cost


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Results: SystemWeather Reconnaissance

  • Key Results:

    • UAS more cost effective than current manned alternative

    • Most important inputs in determining overall cost effectiveness:

      • Mission hours per year

      • Com link cost

      • Operational lifetime

    • Currently available sense-and-avoid equipment cause significant decrease in cost effectiveness, but does not cause the UAS to be more expensive than the manned alternative


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Pipeline Monitoring System

Analyzed the use of the Aero Environment AeroPuma vs. the use of concentric wire sensors ($6+/m)

Note the difference in monitoring style

UAS monitors using thermal imaging with each pass and relays pertinent leak info to docking stations

Concentric sensors constantly monitor pipeline and relay information

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Results: SystemPipeline Monitoring

  • Key Results:

    • UAS cheaper depending on number in use

    • Important to note difference in monitoring styles between UAS and concentric sensor

    • Important inputs:

      • Relay/Docking station cost

      • Number of UASs in use


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Localized Surveillance System

Application based on the surveillance of a 1km2 area for a short time (~1-3 hours)

Considered the use of a Cyber Defense Systems CyberBUG vs. the use of a traffic helicopter

For model inputs, considered monitoring a large traffic accident over 2 hours

For policy considerations, analyzed the addition of mandated sense-and-avoid hardware to the UAS


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Results: SystemLocalized Surveillance

PDF of Cost per Mission for UAS Compared with Manned Alternative

Probability Density

Note: no meaningful overlap










Cost per Mission ($)


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Results: SystemLocalized Surveillance

PDF of Cost per Mission for UAS Compared with Manned Alternative with High-Range Fixed Cost Variance

Probability Density

Note: still no meaningful overlap










Cost per Mission ($)


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Results: SystemLocalized Surveillance

PDF of Cost per Mission for a Larger UAS Capable of Carrying Sense-and-Avoid Equipment Compared with the Cost of Manned Alternative

Probability Density

Note: Significant overlap indicating that UAS would likely no longer be a viable alternative to manned craft









Cost per Mission ($)


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Results: SystemLocalized Surveillance

Missions per Year

Mission Related Costs

Flight Hours Per Mission

Input Importance for Cost Per Mission

Importance of inputs.

Input Costs


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Results: SystemLocalized Surveillance

  • Key Results:

    • UAS less expensive in almost every case

    • Levelized cost for manned more sensitive than to utilization hours & discount rate than cost for unmanned

    • UAS cost effectiveness reduced significantly by requirement for sense-and-avoid hardware

    • Important inputs:

      • Missions per year

      • Discount rate

      • Flight hours per mission


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Policy Implications System

  • Analyzed the effect of the following policies:

    • Mandated insurance premiums

    • Mandated use of A/N hardware

      • (Increased fixed cost)

    • Mandated record-keeping practices

      • (Increased yearly cost)

    • Mandated airframe materials

      • (Increased fixed cost)

    • Mandated minimum amount of safety equipment

      • (Increased fixed cost)

    • Mandated pilot/operator training


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Policy Implications: SystemResults

  • All policies except mandated sense-and avoid hardware had little effect on the cost advantage of UAS over manned alternative

  • Required sense-and-avoid hardware greatly affects cost-effectiveness, however

    • Localized Surveillance and Pipeline Monitoring would no longer be viable as larger, much more expensive UAS would be necessary


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Risk, Technologies, & Standards System

Team Members:

Samiah Akhtar

Jonathan Cornell

Nicole Hayward

Will Kim

Nick Misek

Doug Robl

Team Manager:

Keith Florig


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Purpose System

  • Derive a risk model to explore how risk is related to UAS numbers, dimensions, and flight zones

  • Research on elements of risk mitigation such as human factors, sense and avoid

  • Exploration of alternative incident reporting systems



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Technology and Risk Outline System

  • Goals

  • Risk Modeling

    • Purpose

    • Assumptions & Approach

    • Findings

  • UAS Risk Mitigation

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Risk Modeling Purpose System

  • Provide a way of modeling that creates some groundwork for future modeling

  • Use model to compare relative risk calculations

  • Pointer to the future, not the answer

  • Points of interest

    • Mid-air vs. single-craft crash

    • Effect of sense and avoid technology

    • UAS to displace manned aircraft


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Risk Modeling Assumptions System

  • Uniform national model

  • Uniform traffic density

  • Uniform ground population density

  • Uniform aircraft per type

  • Appropriate for:

    • VFR traffic

    • Rural, less populated areas

  • NOT Appropriate for:

    • Urban settings

    • Airports

    • High traffic densities

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Risk Model System

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Risk Modeling Approach System

Number of midair collisions:

N = total number of aircraft

in defined airspace

ρ = aircraft traffic density

D = diameter of plane


S = average aircraft speed

P(A) = probability of avoidance

(Used for calibration)

VFR operations only

UAV Picture Source:

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Risk Modeling: UAs displacing Manned System

Small risk from unmanned at lower extrema

Risk from unmanned at low levels less than decreased risk from manned

Single-craft crashes still present less risk than mid-airs

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Risk Modeling: Mid-Air vs. Single-Craft System

At some point, manned risk surpasses unmanned risk

At low numbers, sense and avoid has little effect

Single-Craft generally less risk than mid-air

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Risk Modeling Conclusions System

  • Mid-air collisions generally have more risk than single-craft crashes

  • Displacing small to moderate amounts of manned craft represents decrease in risk

  • Smaller, less reliable UAs can present less risk than larger more reliable manned aircraft

  • For small numbers of UAs in low traffic densities, sense and avoid has small effect

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Technology and Risk Outline System

  • Goals

  • Risk Modeling

  • UAS Risk Mitigation

    • Human Factors

    • Sense and Avoid

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Human Factors Implications System

  • Risks - Caused Most Number of Accidents

    • “Sensory Isolation” [McCarley et al]

      • UAS operator does not receive same sensory cues as manned aircraft operator

    • Automation

      • Malfunction of automated components controlled by the UAS operator

    • Operator Hand-Off

      • Issues with handing off control of vehicle from one operator or crew to another

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Human Factors Implications System

  • Recommendations

    • Training and Procedures

      • Up to date training as new technology advancements arise

      • Ensure that operator has accurate knowledge of automated components within UAS

    • Multimodal displays

      • Prevent sensory isolation

      • Allow for audio, visual and speech control

      • Example: simulated cockpit

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Detect, Sense and Avoid System

  • Risks

    • Market impact of single fatal collision

    • Lack of standardization among DSA systems

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Detect, Sense and Avoid System

  • Recommendations

    • Create regulations specific to size, weight, application etc

    • Testing Periods

    • Phased Integration

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Technology and Risk Outline System

  • Issues

  • Goals

  • Risk Modeling

  • UAS Risk Mitigation

    • Reporting systems

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Current Reporting Systems System

  • Two Options

    • NTSB Reporting (as required by FAR) - Accident

    • NASA ASRS Voluntary Reporting - Incident

  • Current Implementation

    • NTSB mandates detailed information when:

      • Flight control system malfunction, Illness of crewmember, Turbine Engine Failure, In-flight fire, Mid-air collision or Damage in excess of $25,000 to other property

    • ASRS System is anonymous and does not have any reporting requirements

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Reporting Recommendations System

  • Initially mandate reporting of all accidents and incidents

  • Re-evaluate strategy after testing period


- NTSB information helps FAA to assess standards

- FAA responds with rules for reporting incidents.

- NTSB provides useful information on UAS failures

- UAS responds with improved design and engineering



UAS manufacturers


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Public Awareness & Perceptions System

Team Members:

Darian Ghorbi

Jenny Kim

Mark Peterson

Laura Seitz

Patrick Snyder

Team Manager:

Pete Tengtrakul


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Statement of Purpose System

  • Add the element of public perception to the discussions of UAS in the NAS

  • Motivation: the fact that there has never been a formal presentation of public perception on the topic

  • Findings: useful for the creation of regulations and policy implications

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Objectives System

  • Compare public perceptions of the risks concerning manned and unmanned aircraft

  • Find demographic groups with certain risk and benefit patterns of UAs

  • Research implications of opinion of UAs

  • Create survey to aid in completing objectives

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Hypotheses System

  • Perceived Risk

    - Manned < Remotely Piloted < Autonomous

  • Ground vs. Air

    - More risk of UAS perceived in air

  • Prior Knowledge vs. Risk Perception

    - Prior knowledge, associate less risk

  • Benefit vs. Risk Perception

    - Higher benefit, lower risk

  • Education vs. Risk Perception

    - Technical education, associate less risk

  • Age vs. Risk Perception

    - Older participants more cautious

  • Frequency of Flight

    - Those that fly frequently, associate less risk

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Layout of Survey System

  • First Page

    • Provide information about UAS

      • Autonomous

      • Remotely Piloted

    • Gauge previous knowledge

      • Source

  • Last Page

    • Demographics

      • Gender

      • Age

      • Education

      • Frequency of Flight

      • Voting (identify opinions of those that are politically engaged)

      • Pilot

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Layout of Survey System

  • Application

  • Traffic Monitoring

  • Pipeline Monitoring

  • Disaster Relief

  • Border Patrol

  • Questions

  • Quick Response

  • Benefit

    • Stakeholder

    • Public

  • Risk

    • Ground

    • Air

  • 7 Point Scale

    • 1 - Much Less

    • 4 - Same

    • 7 - Much More

Picture of UAS application

  • Description of UAS

  • Physical Information

  • Current application

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Obtaining Surveys System

  • Coding: numerical code assigned

  • Screening: data obtained from those under 16 years of age were not counted

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Statistical Methods System

  • Paired T-tests

    • Across applications


    • Significance of mean

  • Regression

  • Correlations

    • Demonstrated the strength of the variables (risk and benefit)

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Results: SystemDescriptive Statistics

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Perceived Relative Risks Between Remotely Operated vs. Autonomous

Autonomous applications are viewed to have more risk in comparison to remotely operated UAs.

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Relative Risks Across Applications Autonomous

Traffic Monitoring has the highest perceived risk.

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Relative Benefit Across Applications Autonomous

The more risky the public perceived the application, the less benefit they associated with the application.

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Relative Perceived Risk to People on Ground vs. Air Autonomous

There is no difference between risk perceived on ground vs. air. Also, there is no difference between perceived benefit between stakeholders and society.

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Demographics and Risk Autonomous

Those over the age of 65 perceived UAs as least risky and least beneficial; the mean value is insignificant.

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Risk Perception Conclusions Autonomous

  • Unmanned aircraft risk > manned

  • Autonomous risk > Remotely piloted

  • No difference:

    • Risk: Ground vs. Air

    • Benefit: Stakeholders vs. Society

  • 54% heard of UAS

    • Need education programs

    • 78% of those that heard of UAS obtained information from television

    • The more familiar, the more comfortable

  • Traffic Monitoring - higher risk

    • Fear of operating around high population density areas

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Impact on Policy Autonomous

  • Limit flight path/area

  • Limited population density

  • Implement education/outreach programs

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Future Insights Autonomous

  • Limitations:

    • Time, Resources, and Budget

  • Sample

    • National scale-different regions

  • Future Surveys:

    • Compare UAS to other risky technologies

    • Size of aircraft

    • Privacy concerns

    • Economics concerns

    • Lengthened

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Governance Autonomous

Team Members:

Nora Darcher

Norma Espinosa

Scott Fortune

Andrea Fuller

Team Manager:

Leonardo Reyes-Gonzalez


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Purpose Autonomous

  • Evaluate current system of governance for UAS integration against principles of good governance

  • Suggest measures that could improve the governance process

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Analysis Autonomous

  • Principles of Good Governance

  • Rules for FAA governance

  • Historical Technologies

  • Actor Interactions

  • Roadblocks

  • Cost and Benefits for each actor

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Characteristics of Good Governance Autonomous

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Governance requirements on FAA Autonomous

  • OMB rule requires FAA standards adoption procedures to have the following:

    • Openness

    • Balance of interest

    • Due process

    • Appeal process

    • Consensus

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Historical Analysis Autonomous

  • How did the governance system handle the introduction of new technologies?


What We Learned

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Actor Analysis Autonomous

  • Objective:

    • Provide a systematic assessment of the actors involved in integrating UAs into the NAS

  • Process:

    • Identified key actors, examined their goals and looked at problem from each actor’s perspective

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Resistant Autonomous




Actor Analysis

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Roadblock Analysis Autonomous

  • Objective: prioritize problems inhibiting the integration of UAs

  • Categories

    • Technological

    • Organizational

    • Infrastructural

    • Public Concern

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Roadblock Analysis Autonomous

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Roadblock Analysis Autonomous

# of actors


Airspace Access



Equivalent/Acceptable Level of Safety





Data Acquisition





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Most obvious needs Autonomous

  • Defining an equivalent/acceptable level of safety

  • Allowing UAS operations in scarcely-used airspace to facilitate testing and development for civil and commercial applications.

  • Potentially large public concern about UAS safety argues for proactive public involvement in deliberations

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Conclusions Autonomous

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Summary of Conclusions Autonomous

  • Economics

  • Risk, Technologies, and Standards

  • Public Awareness and Perception

  • Governance

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Economics Autonomous

  • Some civil UAS applications seem highly competitive with alternatives

  • Initial policy ought to be tailored to the most commercially viable applications

  • Cost models show that (i) costs are most sensitive to hours of utilization, (ii) safety equipment has modest cost effect, except for small systems using sense and avoid

  • Foreign UAS firms may develop an advantage if they gain airspace first

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Risk, Technologies, and Standards Autonomous

  • For some applications in some classes of airspace, unmanned aircraft result in fewer fatalities than manned aircraft used for the same task

  • Sense and avoid is important only in airspace with significant traffic density

  • Low risk areas could be used for experimentation and testing without posing a high risk to those on the ground or in other aircraft

  • A mandatory incident reporting system has potential to greatly improve both airworthiness and human factors reliability

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Public Awareness and Perception Autonomous

  • All UAS applications surveyed were considered more risky and less beneficial than the manned alternative

  • Traffic monitoring perceived as most risky (likely due to flight over dense population)

  • About half of participants had heard of UAs

  • Those more familiar with UAS technology perceive less risk

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Governance Autonomous

  • Integration problem is more complex than many people realize

  • Incremental approach allows for policy experimentation at low risk (e.g., sparsely populated areas/airspace)

  • Standards need to be established to provide benchmark and incentive for manufacturing

  • Attention to public perception and involvement can greatly influence unfolding of UAS issue

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Thank You for Coming! Autonomous